SaaMS: The synopses-as-a-microservice paradigm for scalable adaptive streaming analytics across the cloud to edge continuum - Kalfakis, Georgios Panagiotis; Giatrakos, Nikos (Information systems (Oxford), 2026)
The use of data synopses in Big streaming Data analytics can offer 3 types of scalability: (i) horizontal scalability, for scaling with the volume and velocity of Big streaming Data, (ii) vertical scalability, for scaling with the number of processed streams, and (iii) federated scalability, i.e. reducing the communication cost for performing global analytics across a number of geo-distributed data centers or devices in IoT settings. Despite the aforementioned virtues of synopses, no state-of-the-art Big Data framework or IoT platform provides a native API for stream synopses supporting all three types of required scalability. In this work, we fill this gap by introducing a novel system and architectural paradigm, namely Synopses-as-a-MicroService (SaaMS), for both parallel and geo-distributed stream summarization at scale. SaaMS is developed on Apache Kafka and Kafka Streams and can provide all the required types of scalability together with (i) the ability to seamlessly perform adaptive resource allocation with zero downtime for the running analytics and (ii) the ability to run both across powerful computer clusters and Java-enabled IoT devices. Therefore, SaaMS is directly deployable from applications that either operate on powerful clouds or across the cloud to edge continuum.